CN115912342B - Regional flexible load low-carbon scheduling method based on cloud model - Google Patents
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Abstract
The invention provides a cloud model-based regional flexible load low-carbon scheduling method, which comprises the following steps: firstly, dividing typical daily load data of a demand side into three periods of peak-flat-valley by adopting fuzzy c-clustering; secondly, constructing a flexible load model and an energy storage device model, and calculating user compensation cost based on the flexible load model; then, constructing a micro-grid optimization model according to the energy storage device model, the user compensation cost, the electricity price of the peak-flat-valley period and the carbon emission; and finally, carrying out cross iteration on the micro-grid optimization model through a multisource coordination optimization output strategy and a cloud model evaluation method, and solving an optimal solution with the minimum comprehensive cost. The invention reduces the carbon emission and peak-valley difference of the system, effectively ensures the safety and environmental characteristics, and improves the reasonable consumption of renewable energy sources.
Description
Technical Field
The invention relates to the technical field of resource scheduling in a micro-grid system, in particular to a regional flexible load low-carbon scheduling method based on a cloud model.
Background
The electric power industry is used as a main source of carbon emission, and the pushing energy power is converted to construct an electric power system taking new energy as a main body. The micro-grid has the characteristic of flexible operation, improves the power supply reliability of the distributed power generation system, and reduces the pollution of the system. As micro-grids develop, demand side load research is widely focused as a new hotspot, and flexible load is one of the effective ways to realize supply and demand sides.
Literature [ Wang Ke, yao Jianguo, yao Liangzhong, et al ] overview of electric power flexible load scheduling study [ J ] electric power system automation, 2014,38 (20): 127-135 ] literature [ Huang Kaiting ] flexible loads are classified into industrial, commercial, and residential loads of class 3 in view of their microgrid system capacity optimization configuration [ D ] university of Yanshan, establishing a "source-grid-storage-load" coordinated optimized energy management strategy containing multiple types of flexible loads. Document [ Shao Zhifang, zhao Jiang, zhang Yuqiong ] independent microgrid source-charge coordinated configuration optimization [ J ]. Grid technology, 2021,45 (10): 12 ] forms an energy integrated system in a cogeneration manner, comprehensively optimizing the system from the energy supply side and the demand side, but taking into account only the translatable nature of the load. The literature [ aliasing name ] considers the optimized operation [ J ] of the power grid technology of the distributed combined cooling heating power system with translatable load, 2018,42 (3): 7 ] establishes an independent micro-grid model, and provides an optimal configuration method for achieving the minimization of the comprehensive cost of the system through iterative coordination of a source end model and a load end model. Literature [ Liu Ronghui, horse day, high and far, lianghe, zhu Yuqi, liu Meiyuan ] low-carbon economic scheduling of community integrated energy systems considering demand side cooperative response [ J ]. Shanghai university journal, 2020,36 (5): 10 ] an integrated energy scheduling system comprising flexible loads, storage batteries, gas turbine power generation systems and the like is established based on an energy hub, and from the perspective of community energy operators, the overall cost of the system is the lowest as an objective function of the system running cost and the environmental cost.
The above researches on the flexible load mostly consider that the flexible load is combined with the comprehensive energy system or the user satisfaction and the user demand response, and the researches on low carbon are less, and meanwhile, the researches on the uncertainty of the flexible load demand response on the demand side are also less involved, and the cloud model is a good method for solving the uncertainty of qualitative and quantitative.
The qualitative concept of wind power prediction error is quantitatively expressed by the cloud model theory in consideration of a wind power high-order uncertainty distributed robust optimization scheduling model [ J ]. Electrical engineering theory, 2020,35 (1): 12 ], and a probability density function of wind power plant power error is calculated by utilizing a reverse cloud generator to calculate cloud model characteristic parameters. The method for predicting the short-term wind power based on the wind speed cloud model on the similar day comprises the following steps of [ Yan Jie, xu Chengzhi, liu Yongqian, etc. ] carrying out power system automation, 2018,42 (6): 7 ] describing uncertainty and randomness characteristics of wind speed daily variation by adopting a cloud model theory, constructing a cloud model similarity index, searching optimal sample data, and finally utilizing the sample data to establish a short-term wind power prediction model. Both documents build a cloud model starting from uncertainty of the wind power output on the supply side.
Disclosure of Invention
According to the method, related research is carried out on user response uncertainty from a demand side, a regional flexible load low-carbon scheduling method based on a cloud model is provided, a fuzzy C-means clustering model is established to divide time into peak-average valley periods, a low-carbon scheduling model based on various flexible loads is established, and a PSO algorithm is adopted for solving; the carbon emission and peak-valley difference of the system are reduced, the safety and the environmental characteristics are effectively ensured, and the reasonable consumption of renewable energy sources is improved.
The technical scheme of the invention is realized as follows:
a regional flexible load low-carbon scheduling method based on a cloud model comprises the following steps:
step one: dividing typical daily load data of a demand side into three periods of peak, flat and valley by adopting fuzzy c-clustering;
step two: constructing a flexible load model and an energy storage device model, and calculating user compensation cost based on the flexible load model;
step three: constructing a micro-grid optimization model according to the energy storage device model, the user compensation cost, the electricity price of the peak-flat-valley period and the carbon emission;
step four: and carrying out cross iteration on the micro-grid optimization model through a multisource coordination optimization output strategy and a cloud model evaluation method, and solving an optimal solution with the minimum comprehensive cost.
The implementation method of the first step comprises the following steps:
given a load sample of x j ，j＝1,2,...,n，x j Can be classified into c types, and the classified cluster center vector is L= { L 1 ,l 2 ,...,l c -a }; the degree of each load sample from the clustering center is called membership degree, u is used ij ∈[0,1]Represents x j Is the membership of class i, i=1, 2, …, c, whose objective function is defined according to the euclidean distance; the specific formula is as follows:
where m represents a blur coefficient, d ij ＝||x j -l i The expression of sample x j To the clustering center l i J (U, L) represents the minimum of the objective function that defines the C-cluster algorithm based on the euclidean distance.
The flexible load in the flexible load model comprises translatable load, translatable load and reducible load;
translatable load:
translatable load F shift Each time the scheduling time period unit is 1h, it participates in the power distribution vector before schedulingThe method comprises the following steps:
in the formula ,ts For the initial period, t d For duration, Q shift Is electric power;
the translatable time interval is [ t ] sh- ,t sh+ ]Mu is used to represent F shift In a translational state for a certain period, μ=1 represents the load F shift Beginning translation from τ period; μ=0 represents the load F shift Without translation, set T of start periods shift The method comprises the following steps:
T shift ＝[t sh- ,t sh+ -t d +1]∪{t s }；
if τ=t s The load is unchanged; if tau e t sh -,t sh+ -t d +1]And is also provided withThen from the start period t s Translates to a load F of initial period τ shift The power distribution vector of (2) is:
calculating translatable load user compensation cost C shift ：
in the formula ,is the sum of the power consumption values->Compensating for a unit user corresponding to the translatable load;
load can be transferred:
by the variable mu i,t Representing transferable loadsIn period [ t ] tr- ,t tr+ ]Is μ i,t =1 represents the load Q trains Beginning transition from τ period; mu (mu) i,t =0 represents the load Q trains Not transfer; user compensation fee C after transfer trains The method comprises the following steps:
wherein ,representing a unit user compensation corresponding to the transferable load;
load can be reduced:
variable u i,t Indicating load reductionIf the reduction state of (1) u i ,t =1 indicates that the power is reduced during the period t, and the power during the period t is:
wherein ,load can be cut down before scheduling>The power consumption of the secondary period t; gamma ray i To reduce the load factor, when gamma i =1 indicates complete clipping; user compensation fee C after invocation cut The method comprises the following steps:
wherein ,for the difference after and before clipping +.>Compensating for unit users corresponding to the load reduction.
The energy storage device model is as follows:
S oc (t+1)＝(1-a)S oc (t)-β ES P ES (t)△t/E ES,rated ；
in the formula ：SOC (t+1) is the state of charge of the t+1 period, S OC (t) is the t period state of charge, E Es，rated For battery capacity, P Es (t) is the charge-discharge power of the storage battery, P ES (t) > 0 is discharge, P ES (t) < 0 is charge, a is self-discharge rate, beta ES The charge and discharge efficiency is as follows: and is also provided withβ Es，c For charging the accumulator, beta Es，d The discharge efficiency of the storage battery is achieved.
The micro-grid optimization model is as follows:
min C＝C sy +C dc ；
wherein C is the total cost, C sy Maintenance cost for basic output of system, C dc Is carbon cost;
C sy ＝C net +C f +C ppv +C fc +C de +C gat +C shift +C trains +C′ cut ；
in the formula ：Cnet For the power grid interaction cost, C f C, for the running cost of the wind turbine generator system ppv For the operation cost of the photovoltaic unit, C fc ，C de For the cost of the spare machine set of the system, C gat For the cost of the carbon capturing unit, f net For the interaction coefficient of the power grid, f f For the cost coefficient of the wind turbine ppv Is the cost coefficient of the photovoltaic generator set, f fc 、f de As a cost coefficient of the standby unit, f gat The coefficient of the carbon capturing unit; p (P) net For each period of power of the power grid, P f For the output power of the wind turbine generator, P ppv For the output power of the photovoltaic unit, P fc 、P de To the output power of the standby unit, P gat The operation power of the carbon capture unit is M, and the M is a scheduling period;
C dc ＝C cnet +C cfc +C cde +C cdeal ；
C cnet ＝f cnet K net ；
C cfc ＝f ccfc K fc ；
C cde ＝f cde K de ；
C cdeal ＝K t (R out -R all )；
in the formula ：Ccnet CO generated for grid interactions 2 Cost, C cfc CO generated by fuel cell of standby unit 2 Cost, C cde CO generated for diesel generator output 2 Cost, C cdeal Is the carbon transaction weight of the system, f cnet Average CO emission for power grid 2 Intensity coefficient f c fc is the average emission of CO from the fuel cell 2 Intensity coefficient f cde Average CO emission for diesel generator 2 Intensity coefficient, K net For electric network CO 2 Discharge amount, K fc For fuel cell CO 2 Discharge amount, K de Is a diesel generator CO 2 Discharge amount, K t For the market price of carbon trade on the same day, R out As total CO 2 Discharge amount, R all Is a carbon weight quota;
constraints of the microgrid optimization model include:
power balance constraint:
P net +P f +P ppv +P fc +P de ＝P sa +P w ；
in the formula ：Pw P is typical daily load information sa Load capacity for the energy storage device;
unit power constraint:
P net，min ≤P net ≤P net，max ；
0≤P f ≤P f，max ；
0≤P ppv ≤P ppv，max ；
0≤P fc ≤P fc，max ；
0≤P de ≤P de，max ；
0≤P gat ≤P gat，max ；
in the formula ,Pnet，min For the lower limit of the interaction power of the power grid, P net，max For the upper limit of the interaction power of the power grid, P f，max For the upper limit of the output power of the wind turbine generator, P ppv，max For the upper limit of output power of the photovoltaic unit, P fc，max 、P de，max Are all upper limits of the output power of the standby unit, P gat，max The upper limit of the operation power of the carbon capture unit is set;
energy storage device constraint:
S sa，min ≤S sa ≤S sa，max ；
P csoc P fsoc ＝0；
wherein ,Ssa To store the state of charge of the device, S sa，min For storing minimum state of charge of the device, S sa，max To store the maximum state of charge of the device, P csoc Is the state of charge per unit time, P fsoc A discharge state per unit time;
load constraint:
wherein ,representing the minimum value of the transferable load power, < >>Representing the maximum value of the transferable load power, +.>Representing the minimum linkRun time, v i，t Representing the amount of shift not within the transferable load interval,/->Indicating the start phase of transferable load->Indicating the transferable load end phase,/->Representing a minimum continuous cut-down time, < > in >>Represents the maximum continuous reduction time, N max Indicating the maximum number of cuts.
The implementation method of the fourth step is as follows:
s4.1, inputting typical daily load data, processing data of each unit and CO consumption of each unit 2 Data, time-of-use electricity price data and demand side flexible load distribution data;
s4.2, conducting time-of-use electricity price guidance and flexible load modeling on the demand side, and meanwhile analyzing the uncertainty of demand response on the load after the demand response by adopting a cloud model;
s4.3, solving the load after demand response by adopting a multisource coordination optimization output strategy to obtain the optimal configuration of each unit;
and S4.4, substituting the optimal configuration of each unit into a micro-grid optimization model to calculate the total cost, and repeating the steps S4.2-S4.4 until an optimal solution is obtained.
The multisource coordination optimization force strategy is as follows:
s4.3.1, comparing the wind-solar cost with the time-sharing electricity price, when the electricity price of the power grid is low, preferentially adopting a mode of purchasing electricity to the upper power grid, and calculating the carbon cost;
s4.3.2, considering the charge and discharge efficiency of the storage device, if the charge state of the storage device is smaller than a set value, charging the storage device preferentially, and if the storage device reaches the maximum capacity and has surplus electric quantity, selling electricity to the power distribution network; simultaneously detecting the load value of the storage device, and stopping discharging when the load value reaches a set minimum value;
s4.3.3, considering the standby load unit, and comprehensively obtaining the optimal unit combined output according to a low-carbon target and an economic strategy.
The method for analyzing the uncertainty of the demand response by adopting the cloud model comprises the following steps:
establishing a cloud generator for sample data by using the user demand response quantity after flexible load scheduling:
1) Generating an expected value E based on the sample data n Standard deviation is H e Normal random number E 'of (2)' n ；
2) Generating a desired value Ex based on the sample data 、 abs(E′ n ) A normal random number x which is a standard deviation;
3) Let x be the primary quantization value of qualitative concept, become cloud droplet;
4) Calculating y=exp [ - (x-E) x ) 2 /2(E′ n ) 2 ]；
5) Let y be the certainty that x belongs to the qualitative concept;
6) Repeating steps 1) through 5) until P cloud droplets are produced.
Compared with the prior art, the invention has the beneficial effects that:
(1) According to the invention, a demand response uncertainty cloud model of a user at a demand side is considered, and the relation between ambiguity and randomness is revealed through the description of digital features, expected qualitative concept points, the description of entropy to uncertainty and the description of super entropy to randomness of a concept sample in a cloud chart, so that the response features of the user under different flexible load scheduling excitation are reflected; according to the cloud picture, the user response amount gradually becomes stable along with the increase of the flexible load in the dispatching system, and the cohesion degree also gradually increases.
(2) By means of a peak-flat-valley dividing method of fuzzy c-clustering, flexible load scheduling and the like, reasonable utilization efficiency of energy sources at the demand side is improved, peak-valley difference of a user electricity utilization curve is reduced, and safety and economical efficiency of an electric power system are improved.
(3) According to the environmental protection requirement, under the unit operation output decision taking economic cost and carbon emission into consideration, starting from the two aspects of a demand side and a supply side, taking a load with flexible characteristics and a low-carbon target as a scheduling means, and taking the minimum total cost as a target to distribute the flexible load and a coordination decision of source-net-storage-load between each unit, so as to construct an optimized energy management strategy containing multiple types of flexible loads; the flexible load is verified to be flexible, the schedulability is high, the system cost is reduced, meanwhile, the carbon capture unit is added, the system is more environment-friendly, meanwhile, the system cost is further reduced, and the method has good engineering application significance.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic view of a cloud model of the present invention.
FIG. 2 is a diagram of a multi-source coordinated optimization output configuration of the present invention.
Fig. 3 is a flowchart of solving a multisource coordination optimization output model under a micro-grid.
FIG. 4 shows the predicted power on the demand side of the present invention.
FIG. 5 is a predicted value of the output of a wind turbine and a photovoltaic unit according to the present invention.
Fig. 6 is a flexible load profile of the present invention.
Fig. 7 is a user load classification diagram of the present invention.
FIG. 8 is a graph showing a flexible load free dual carbon free scheduler set of the present invention.
FIG. 9 is a graph of a flexible load non-dual carbon dispatch system of the present invention.
FIG. 10 is a graph of cost versus analysis of the present invention.
FIG. 11 is a user response cloud model under different stimuli of the present invention; wherein, (a) is free of flexible load, (b) is translatable load, (c) is translatable load, and (d) is translatable load, and is load shedding.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention provides a regional flexible load low-carbon scheduling method based on a cloud model, which reduces carbon emission and peak-valley difference of a system, effectively ensures safety and environmental characteristics, and improves reasonable consumption of renewable energy sources. The method comprises the following specific steps:
step one: dividing typical daily load data of a demand side into three periods of peak, flat and valley by adopting fuzzy c-clustering;
most adopt a fixed time interval dividing method according to the distribution rule of the load curve. However, the invention considers the dynamic time interval dividing method because of the randomness of the new energy output and the fluctuation of the loads of both supply and demand parties in the micro-grid. In order to fully mine scheduling potential of the demand side, the upper-level distribution network can reasonably divide the daily load of the demand side by adopting a fuzzy c-clustering method, divide the electricity consumption of the demand side into three periods of peak, flat and valley, and adopt a method of dividing the time-sharing electricity price by following the dynamic period so as to achieve the purpose of guiding the time-sharing electricity price. In practice, the load prediction information can be divided in advance, and the user receives the electricity consumption plan and implements the electricity consumption plan.
Let the load sample be x j ，j＝1,2,...,n，x j Can be classified into c types, and the classified cluster center vector is L= { L 1 ,l 2 ,...,l c -a }; the degree of each load sample from the clustering center is called membership degree, u is used ij ∈[0,1]Represents x j Is the membership of class i, i=1, 2, …, c, whose objective function is defined according to the euclidean distance; the specific formula is as follows:
in the formula, m represents a blur coefficient, and the greater m is, the higher the degree of blur of the cluster is. d, d ij ＝||x j -l i The expression of sample x j To the clustering center l i J (U, L) represents the minimum of the objective function that defines the C-cluster algorithm based on the euclidean distance.
Step two: constructing a flexible load model and an energy storage device model, and calculating user compensation cost based on the flexible load model;
the flexible load participates in the dispatching, so that the balance of power between the stabilizing load of the storage battery and the output of the renewable energy source can be coordinated, and the synergistic effect among the renewable energy source, the storage battery and the load is effectively realized. Based on the user's autonomous response characteristics, the flexible loads can be classified into the following categories:
translatable load:
assuming translatable load F shift Each time the scheduling time period unit is 1h, it participates in the power distribution vector before schedulingThe method comprises the following steps:
in the formula ,ts For the initial period (in h), t d For duration (in h), Q shift For using electric power.
Assume that the translatable period interval is [ t ] sh- ，t sh+ ]Since an overall translation is required, F is represented by the 0-1 variable μ shift In a translational state for a certain period, μ=1 represents the load F shift Beginning translation from τ period; μ=0 represents the load F shift Without translation, set T of start periods shift The method comprises the following steps:
T shift ＝[t sh- ，t sh+ -t d +1]∪{t s } (5)；
if τ=t s The load is unchanged; if tau e t sh- ，t sh+ -t d +1]And is also provided withThen from the start period t s Translates to a load F of initial period τ shift The power distribution vector of (2) is:
calculating translatable load user compensation cost C s7hift ：
in the formula ,is the sum of the power consumption values->Compensating for the unit user corresponding to the translatable load.
Load can be transferred:
with variable mu from 0 to 1 i，t Representing transferable loadsIn period [ t ] tr- ，t tr+ ]Is μ i，t =1 represents the load Q traind Beginning transition from τ period; v i，t =0 represents the load Q trains Not transfer; user compensation fee C after transfer trains The method comprises the following steps:
wherein ,representing the unit user compensation corresponding to the transferable load.
Load can be reduced:
setting a variable u of 0-1 i，t Indicating load reductionIf u is the reduction state of i，t =1 indicates that the power is reduced during the period t, and the power during the period t is:
wherein ,load can be cut down before scheduling>The power consumption of the secondary period t; gamma ray i To reduce the load factor, when gamma i =1 indicates complete clipping, 0 < γ i < 1; user compensation fee C after invocation cut The method comprises the following steps:
wherein ,for the difference after and before clipping +.>Compensating for unit users corresponding to the load reduction.
Energy storage device model:
the general model of the storage battery is as follows:
S oc (t+1)＝(1-a)S oc (t)-β ES P ES (t)Δt/E ES，rated (11)；
in the formula ：SOC (t+1) is the state of charge of the t+1 period, S OC (t) is the t period state of charge, E ES，rated For battery capacity, P ES (t) is the charge-discharge power of the storage battery, P ES (t) > 0 is discharge, P ES (t) < 0 is charge, a is self-discharge rate, beta ES The charge and discharge efficiency is as follows:
wherein ,βS，c For charging the accumulator, beta S，d The discharge efficiency of the storage battery is achieved.
Step three: constructing a micro-grid optimization model according to the energy storage device model, the user compensation cost, the electricity price of the peak-flat-valley period and the carbon emission;
objective function: the system running costs include distributed power costs, electricity purchase costs, user compensation costs, storage device maintenance costs, carbon emissions, and carbon capture unit costs. The objective function is as follows:
min C＝C sy +C dc (13)；
wherein C is the total cost, cs y Maintenance cost for basic output of system, C dc Is the carbon cost.
C sy ＝C net +C f +C ppv +C fc +C de +C gat +C shift +C trains +C cut (14)；
in the formula ：Cnet For the power grid interaction cost, C f C, for the running cost of the wind turbine generator system ppv For the operation cost of the photovoltaic unit, C fc ，C de For the cost of the spare machine set of the system, C gat For the cost of the carbon capturing unit, f net For the interaction coefficient of the power grid, f f For the cost coefficient of the wind turbine ppv Is the cost coefficient of the photovoltaic generator set, f fc 、f de As a cost coefficient of the standby unit, f gat The coefficient of the carbon capturing unit; p (P) net For each period of power of the power grid, P f For the output power of the wind turbine generator, P ppv For the output power of the photovoltaic unit, P fc 、P de To the output power of the standby unit, P gat And (3) the operation power of the carbon capture unit is calculated, and M is a scheduling period.
C dc ＝C cnet +C cfc +C cde +C cdeal (21)；
C cnet ＝f cnet K net (22)；
C cfc ＝f c f c K fc (23)；
C cde ＝f cde K de (24)；
C cdeal ＝K t (R out -R all ) (25)；
in the formula ：Ccnet CO generated for grid interactions 2 Cost, C cfc CO generated by fuel cell of standby unit 2 Cost, C cde CO generated for diesel generator output 2 Cost, C cdeal Is the carbon transaction weight of the system, f cnet Average CO emission for power grid 2 Intensity coefficient f cfc Average CO emission for fuel cells 2 Intensity coefficient f cde Average CO emission for diesel generator 2 Intensity coefficient, K net For electric network CO 2 Discharge amount, K fc For fuel cell CO 2 Discharge amount, K de Is a diesel generator CO 2 Discharge amount, K t For the market price of carbon trade on the same day, R out As total CO 2 Discharge amount, R all Is the carbon weight quota.
Constraint conditions:
power balance constraint:
P net +P f +P ppv +P fc +P de ＝P sa +P w (26)；
in the formula ：Pw P is typical daily load information sa And the load of the energy storage device.
Unit power constraint:
P net，min ≤P net ≤P net，max (27)；
0≤P f ≤P f，max (28)；
0≤P ppv ≤P ppv，max (29)；
0≤P fc ≤P fc，max (30)；
0≤P de ≤P de，max (31)；
0≤P gat ≤P gat，max (32)；
in the formula ,Pnet，min For the lower limit of the interaction power of the power grid, P net，max For the upper limit of the interaction power of the power grid, P f，max For the upper limit of the output power of the wind turbine generator, P ppv，max For the upper limit of output power of the photovoltaic unit, P fc，max 、P de，max Are all upper limits of the output power of the standby unit, P gat，max The upper limit of the operation power of the carbon capture unit is set.
Energy storage device constraint:
state of charge constraints for energy storage devices:
S sa，min ≤S sa ≤S sa，max (33)；
P csoc P fsoc ＝0 (34)；
wherein ,Ssa To store the state of charge of the device, S sa，min For storing minimum state of charge of the device, S sa，max To store the maximum state of charge of the device, P csoc Is the state of charge per unit time, P fsoc A discharge state per unit time; p (P) csoc .P fsoc The value is 0 or 1, which indicates that the battery is charged or discharged in unit time.
Load constraint:
load constraints can be transferred:
1) Load power range constraint
wherein ,representing the minimum value of the transferable load power, < >>Representing the maximum value of the transferable load power.
2) Minimum duration constraint
wherein ,for the minimum continuous operation time, frequent operation of equipment is avoided, and maintenance cost is increased.
3) Transferable load interval constraints
wherein ,vi，t Represents the amount of shift that is not within the transferable load interval,indicating the start-up phase of the transferable load,indicating a transferable end-of-load phase.
Load carrying times and time can be reduced to carry out constraint:
1) Minimum duration constraint
wherein ,representing a minimum continuous blanking time.
2) Maximum duration constraint
Representing the maximum continuous clipping time. />
3) Number of times constraint
wherein ,Nmax Indicating the maximum number of cuts.
Step four: and carrying out cross iteration on the micro-grid optimization model through a multisource coordination optimization output strategy and a cloud model evaluation method, and solving an optimal solution with the minimum comprehensive cost.
Multisource coordination optimization output strategy under low-carbon target: the invention provides a unit output strategy according to a low-carbon energy-saving concept, which mainly comprises the following steps: wind power generation, photovoltaic power generation, fuel cells, diesel generator sets and storage devices. On the premise of meeting the constraint of each unit of the system at the demand side and guaranteeing the power supply reliability, the flexible load at the user side is reasonably scheduled. The following principle is followed:
(1) When the wind-solar cost and the time-sharing electricity price are compared, the electricity purchasing mode of an upper-level power grid can be preferentially adopted when the electricity price of the power grid is low, and partial carbon cost calculation of the power grid output is considered to be within the cost, so that each unit can realize the load demand by adopting a combined output method;
(2) Considering the charge and discharge efficiency of the storage device, if the charge state of the storage device is smaller than a set value, the storage device is charged preferentially, and if the storage device reaches the maximum capacity and has surplus electric quantity, electricity is sold to the distribution network. Simultaneously detecting the load value of the storage device, and stopping discharging when the load value reaches a set minimum value;
(3) And considering the standby load unit, and comprehensively obtaining the optimal unit combined output according to a low-carbon target and an economic strategy.
The specific structure is shown in fig. 2: the method aims at the multisource coordination optimization output strategy, so that the cost of the demand side can be reasonably reduced, the peak-valley difference of the load is basically reduced, and the load utilization rate is greatly improved from the demand side. The carbon trapping unit is used as a low-carbon target realization method to reasonably discharge CO 2 And (5) absorption treatment. The typical post-combustion carbon capture flow mainly comprises 3 main links of absorption, regeneration and compression, and under the premise of not considering the regeneration and the compression, the carbon complement unit adopts independent energy to supply power, carbon capture cost and carbon consumed by absorption are calculated, a part of carbon cost is offset, meanwhile, the system cost is further reduced through carbon transaction, and the system scheduling cost is minimized under the simultaneous effects of three aspects of unit operation decision, flexible load and low carbon implementation.
The method for analyzing the uncertainty of the demand side based on the cloud model comprises the following steps:
because the demand response of each time period at the user side is not a single discrete problem, but is related to psychological effects, real-time electricity utilization conditions and the like of the user, the relation between the quantity and the certainty under excitation can be accurately described by using the cloud model, and therefore, the invention analyzes the optimal output of each unit by using an electricity decision and simultaneously establishes an uncertainty response model based on the cloud model. With a forward cloud generator, N specific cloud droplets are generated to represent a probability distribution map of a concept, as shown in FIG. 1.
"uncertainty of user response under stimulus" is described according to the parameters in fig. 1, where Ex represents expectations, which is a point in the domain that is the most characteristic of qualitative concepts, to reflect the mean characteristic of user response. En represents entropy, which is used for representing measurable granularity of qualitative concepts and reflects uncertainty degree of the qualitative concepts, and is used for representing discrete degree and ambiguity degree of cloud drops relative to Ex in the figure and reflecting probability distribution characteristics of user response. He represents super entropy, which is used to represent the degree of uncertainty of entropy, reflect the randomness of the appearance of qualitative conceptual value samples, and in fig. 1 He is used to reflect the uncertainty of the probability distribution variance of user response.
For the specific concept of "uncertainty of user response under stimulusThe number field of which is denoted by the set U, x is the pair +.>Is a quantized representation of->Is a representation of the degree of uncertainty that, in a cloud image,the transformation is a cloud drop, and is one-time quantization implementation of a specific concept. Establishing a cloud generator for sample data by using the user demand response quantity after flexible load scheduling:
1) Generating an expected value E based on the sample data n Standard deviation is H e Normal random number E 'of (2)' n ；
2) Generating an expected value E based on the sample data x 、abs(E′ n ) A normal random number x which is a standard deviation;
3) Let x be the primary quantization value of qualitative concept, become cloud droplet;
4) Calculating y=exp [ - (x-E) x ) 2 /2(E′ n ) 2 ]；
5) Let y be the certainty that x belongs to the qualitative concept;
6) Repeating steps 1) through 5) until P cloud droplets are produced.
Multi-source coordination optimization output strategy solving under micro-grid
The distribution of the load on the demand side in each period is changed through the guidance of time-of-use electricity price and flexible load scheduling, a particle swarm algorithm is adopted, and the optimal solution with the minimum comprehensive cost is obtained through the cross iteration updating of a multisource coordination optimization output strategy and a cloud model evaluation method, wherein a solving flow chart is shown in fig. 3, and the steps are as follows:
(1) Inputting typical daily load data, processing data of each unit such as wind and light, and the like, and consuming CO by each unit 2 Data, time-of-use electricity price data and demand side flexible load distribution data.
(2) And guiding the time-of-use electricity price and flexible load scheduling on the demand side, and simultaneously analyzing the uncertainty of the demand response on the load after the demand response by adopting a cloud model.
(3) And solving the load after the demand response by adopting a multisource coordination optimization output strategy to obtain the optimal configuration of each unit.
(4) Substituting the optimal configuration of each unit into the total cost calculation including the operation cost and the carbon cost, and repeating the steps (2) - (4) until an optimal solution is obtained.
Calculation case analysis
Basic data: according to the invention, the daily electricity load of a certain community is predicted and managed, the unit output of each time period in a day is simulated, wind and light are used as new energy sources for power supply, a fuel cell is used as a standby load power supply unit, and the power storage device realizes the maximization of load utilization through charge and discharge. Factors such as basic load, flexible load, user compensation, electricity purchasing and selling of the power grid, uncertainty of demand response and the like are comprehensively considered. 24 time periods are set, each hour is 1 scheduling time period, the charge state of the electric storage device exceeds 0.4, the capacity is 160, the charge-discharge energy efficiency is 0.95, and the self-loss coefficient is 0.001. The unit data are shown in Table 2. The predicted power of a certain day at the demand side is shown in fig. 4, and the upper and lower limits of the output of the wind-solar unit are shown in fig. 5.
The invention adopts time-of-use electricity price, the price is shown in table l in detail, the distributed power supply parameters are shown in table 2, the specific data of the flexible load are shown in table 3, and the specific distribution of the flexible load is shown in fig. 6.
Table 1 Power grid Interactive price (Yuan/kWh)
TABLE 2 partial Unit parameters
TABLE 3 Flexible load parameter a translatable load
b load transferable
c load reduction is possible
In order to verify the rationality of the model provided by the invention, the following three cases are set for comparison analysis:
(1) The method comprises the steps that a supply-demand interaction scheduling platform of a carbon capture unit under the condition of not containing flexible load and not containing a low-carbon target is used for performing scheduling analysis, and the uncertainty of demand response is analyzed through a cloud model;
(2) The supply-demand interactive scheduling platform comprises a flexible load and a carbon capture unit without a low-carbon target, performs scheduling analysis on the supply-demand interactive scheduling platform, and analyzes the uncertainty of demand response of the supply-demand interactive scheduling platform through a cloud model;
(3) The supply-demand interactive scheduling platform comprises a flexible load and a carbon capture unit under a low-carbon target, and is used for scheduling and analyzing the supply-demand interactive scheduling platform and analyzing the uncertainty of demand response of the supply-demand interactive scheduling platform through a cloud model.
Analysis of results
The method of fuzzy c clustering is used to divide the load into 3 time periods according to the load size, m is taken to be 2, and the result is shown in fig. 7.
TABLE 4 updated membership matrix
The electricity prices can be divided into a normal period, a valley period and a peak period by the updated membership matrix in the table 4, and the specific electricity prices are shown in the table 1. The results show that 24-7 points are divided into valley periods, 8-11 points and 15-18 points are normal periods, and 12-14 points and 19-23 points are peak periods.
The PSO algorithm is adopted for simulation verification on the above cases, and the obtained results are shown in figures 8 and 9: under the condition of no flexible load and no carbon capturing unit, the peak-valley difference of the system can be seen to be larger, and only a simple output decision is made on the system, so that the cost and the carbon emission of the system are minimized. At the moment of low electricity price, the power is purchased from the power grid as much as possible, and when the electricity price is high, new energy is adopted as much as possible to generate power. While there is a transferable load in each period of load, load shedding or interruption, further scheduling of such loads may result in further system cost reductions.
In the invention, carbon emission of the photovoltaic and wind power is negligible in the using stage, carbon emission coefficients of a power grid, a fuel cell and a diesel engine are 790g/kWh,448g/kWh and 648g/kWh respectively, and CO2 emission tax is 50 yuan/t. Some parameters of the carbon capture unit are shown in table 5, and some carbon emission allowance coefficients are shown in table 6.
TABLE 5 carbon capture unit partial parameter set
TABLE 6 carbon emission quota coefficient
TABLE 7 optimization results
By scheduling the flexible load on the daily demand side, the peak-valley difference of the load is greatly reduced, the load distribution of the power system is reasonably distributed, the load curve is more stable, and the system cost is reduced. As can be seen from table 7, case 1 has a cost of 2153 yuan for the unit operation decision alone without any optimization. As can be seen from fig. 10, case 2 further reduces the cost by considering the user compensation cost through flexible load scheduling for each period, to 2009 yuan, the cost is reduced by 5%, while case 3 considers low-carbon scheduling, and the unit CO is processed in the carbon capturing unit 2 Under the condition of the operation energy consumption of (2), the surplus carbon emission quota is sold to other units according to the market price of single-day carbon trade, the cost is 1602 yuan, and the cost is reduced by 25 percent compared with the initial cost.
Cloud model-based demand response uncertainty analysis: as can be seen from the analysis of FIG. 11, the cloud drop number is set to 1500, and the user in the invention can stably reach the load interval assigned by the dispatching center under the excitation or user compensation for determining the degree of excitation of the load without flexible load and with 1,2 and 3 flexible loads, thereby being beneficial to stabilizing the load on the power supply and distribution side of the system and reasonably using electricity. Fig. 11 (a) and (b) are wider than the distribution, the span is maximum, and the distribution of 0kw/h to 100kw/h and 500kw/h to 600kw/h user loads is denser than that of fig. 11 (b) and (c) and (d) at the leftmost side and the rightmost side of fig. 11 (a), reflecting that the user loads are not concentrated enough under no excitation, the span is larger, and the regulation of the load on the demand side is not facilitated, and compared with that, the cohesion degree of fig. 11 (c) and (d) is higher. Considering that in practical situations, the reduction of comfort brought by load reduction to users in the micro-grid is proportional to the reduction of load, and the comparison of the reference line with the definition of 0.5 as the reference line is compared with the reference line in fig. 11 (c) and (d), the user in fig. 11 (d) has higher cohesion at the reference line, shows the compliance of residents to load excitation, and accords with practical significance. In a comprehensive view, the user certainty under the condition of containing three kinds of flexible load scheduling is higher, and the stable regulation and control of regional power consumption are facilitated.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (7)
1. The regional flexible load low-carbon scheduling method based on the cloud model is characterized by comprising the following steps of:
step one: dividing typical daily load data of a demand side into three periods of peak, flat and valley by adopting fuzzy c-clustering;
step two: constructing a flexible load model and an energy storage device model, and calculating user compensation cost based on the flexible load model;
step three: constructing a micro-grid optimization model according to the energy storage device model, the user compensation cost, the electricity price of the peak-flat-valley period and the carbon emission;
the micro-grid optimization model is as follows:
min C＝C sy +C dc ；
wherein C is the total cost, C sy Maintenance cost for basic output of system, C dc Is carbon cost;
C sy ＝C net +C f +C ppv +C fc +C de +C gat +C shift +C trains +C cut ；
in the formula ：Cnet For the power grid interaction cost, C f C, for the running cost of the wind turbine generator system ppv For the operation cost of the photovoltaic unit, C fc 、C de For the cost of the spare machine set of the system, C gat For carbon capturing unit cost, C shift Compensating for translatable load users, C trains To compensate for the costs on the user side after transfer, C cut To compensate the cost for the called user, f net For the interaction coefficient of the power grid, f f For the cost coefficient of the wind turbine ppv Is the cost coefficient of the photovoltaic generator set, f fc 、f de As a cost coefficient of the standby unit, f gat The coefficient of the carbon capturing unit; p (P) net For each period of power of the power grid, P f For the output power of the wind turbine generator, P ppv For the output power of the photovoltaic unit, P fc 、P de To the output power of the standby unit, P gat For the running power of the carbon capture unit, M is scheduleA time period;
C dc ＝C cnet +C cfc +C cde +C cdeal ；
C cnet ＝f cnet K net ；
C cfc ＝f cfc K fc ；
C cde ＝f cde K de ；
C cdeal ＝K t (R out -R all )；
in the formula ：Ccnet CO generated for grid interactions 2 Cost, C cfc CO generated by fuel cell of standby unit 2 Cost, C cde CO generated for diesel generator output 2 Cost, C cdeal Is the carbon transaction weight of the system, f cnet Average CO emission for power grid 2 Intensity coefficient f cfc Average CO emission for fuel cells 2 Intensity coefficient f cde Average CO emission for diesel generator 2 Intensity coefficient, K net For electric network CO 2 Discharge amount, K fc For fuel cell CO 2 Discharge amount, K de Is a diesel generator CO 2 Discharge amount, K t For the market price of carbon trade on the same day, R out As total CO 2 Discharge amount, R all Is a carbon weight quota;
constraints of the microgrid optimization model include:
power balance constraint:
P net +P f +P ppv +P fc +P de ＝P sa +P w ；
in the formula ：Pw P is typical daily load information sa Load capacity for the energy storage device;
unit power constraint:
P net，min ≤P net ≤P net，max ；
0≤P f ≤P f，max ；
0≤P ppv ≤P ppv，max ；
0≤P fc ≤P fc，max ；
0≤P de ≤P de，max ；
0≤P gat ≤P gat，max ；
in the formula ,Pnet，min For the lower limit of the interaction power of the power grid, P net，max For the upper limit of the interaction power of the power grid, P f，max For the upper limit of the output power of the wind turbine generator, P ppv，max For the upper limit of output power of the photovoltaic unit, P fc，max 、P de，max Are all upper limits of the output power of the standby unit, P gat，max The upper limit of the operation power of the carbon capture unit is set;
energy storage device constraint:
S sa，min ≤S sa ≤S sa，max ；
P csoc P fsoc ＝0；
wherein ,Ssa Is the charge state of the energy storage device, S sa，min S is the minimum charge state of the energy storage device sa，max For maximum state of charge of the energy storage device, P csoc Is the state of charge per unit time, P fsoc A discharge state per unit time;
load constraint:
wherein ,representing the minimum value of the transferable load power, < >>Representing the maximum value of the transferable load power,for load transfer->Representing minimum continuous run time, v i,t Representing the amount of shift not within the transferable load interval,/->Indicating the start phase of transferable load->Indicating the transferable load end phase,/->Representing a minimum continuous cut-down time, < > in >>Represents the maximum continuous reduction time, N max Representing the maximum number of cuts; variable mu i,t Representing transferable load->In period [ t ] tr- ,t tr+ ]Is a running state of (2); u (u) i,t Representing load reducible +.>A clipping state at a period t;
step four: and carrying out cross iteration on the micro-grid optimization model through a multisource coordination optimization output strategy and a cloud model evaluation method, and solving an optimal solution with the minimum comprehensive cost.
2. The cloud model-based regional flexible load low-carbon scheduling method of claim 1, wherein the implementation method of the step one is as follows:
given a load sample of x j ，j＝1,2,...,n，x j Can be classified into c types, and the classified cluster center vector is L= { L 1 ,l 2 ,...,l c -a }; the degree of each load sample from the clustering center is called membership degree, u is used ij ∈[0,1]Represents x j Is the membership of class i, i=1, 2, …, c, whose objective function is defined according to the euclidean distance; the specific formula is as follows:
wherein m represents a blur coefficient, d ij ＝||x j -l i The expression of sample x j To the clustering center l i J (U, L) represents defining C-mers based on Euclidean distanceThe objective function minimum of the class algorithm.
3. The cloud model based regional flexible load low-carbon dispatch method of claim 1, wherein the flexible loads in the flexible load model comprise translatable loads, and reducible loads;
translatable load:
translatable load F shift Each time the scheduling time period unit is 1h, it participates in the power distribution vector before schedulingThe method comprises the following steps:
in the formula ,ts For the initial period, t d For duration, Q shift Is electric power;
the translatable time interval is [ t ] sh- ,t sh+ ]Mu is used to represent F shift In a translational state for a certain period, μ=1 represents the load F shift Beginning translation from τ period; μ=0 represents the load F shift Without translation, set T of start periods shift The method comprises the following steps:
T shift ＝[t sh- ,t sh+ -t d +1]∪{t s }；
if τ=t s The load is unchanged; if tau e t sh- ,t sh+ -t d +1]And is also provided withThen from the start period t s Translates to a load F of initial period τ shift The power distribution vector of (2) is:
calculating translatable load user compensation cost C shift ：
in the formula ,is the sum of the power consumption values->Compensating for a unit user corresponding to the translatable load;
load can be transferred:
by the variable mu i,t Representing transferable loadsIn period [ t ] tr- ,t tr+ ]Is μ i,t =1 represents the load Q trains Beginning transition from τ period; mu (mu) i,t =0 represents the load Q trains Not transfer; user compensation fee C after transfer trains The method comprises the following steps:
wherein ,representing a unit user compensation corresponding to the transferable load;
load can be reduced:
variable u i,t Indicating load reductionIf u is the reduction state of i,t =1 indicates that the time period t is reduced, at this time periodthe power of t is:
wherein ,load can be cut down before scheduling>The power consumption in the period t; gamma ray i To reduce the load factor, when gamma i =1 indicates complete clipping; user compensation fee C after invocation cut The method comprises the following steps:
wherein ,for the difference after and before clipping +.>Compensating for unit users corresponding to the load reduction.
4. The cloud model-based regional flexible load low-carbon scheduling method of claim 3, wherein the energy storage device model is:
S oc (t+1)＝(1-a)S oc (t)-β ES P ES (t)Δt/E ES,rated ；
in the formula ：SOC (t+1) is the state of charge of the t+1 period, S OC (t) is the t period state of charge, E ES,rated For battery capacity, P ES (t) is the charge-discharge power of the storage battery, P ES (t) > 0 is discharge, P ES (t) < 0 is charge, a is self-discharge rate, beta ES The charge and discharge efficiency is as follows: and is also provided withβ ES,c For charging the accumulator, beta ES,d The discharge efficiency of the storage battery is achieved.
5. The cloud model-based regional flexible load low-carbon scheduling method of claim 1, wherein the implementation method of the fourth step is as follows:
s4.1, inputting typical daily load data, processing data of each unit and CO consumption of each unit 2 Data, time-of-use electricity price data and demand side flexible load distribution data;
s4.2, conducting time-of-use electricity price guidance and flexible load modeling on the demand side, and meanwhile analyzing the uncertainty of demand response on the load after the demand response by adopting a cloud model;
s4.3, solving the load after demand response by adopting a multisource coordination optimization output strategy to obtain the optimal configuration of each unit;
and S4.4, substituting the optimal configuration of each unit into a micro-grid optimization model to calculate the total cost, and repeating the steps S4.2-S4.4 until an optimal solution is obtained.
6. The cloud model-based regional flexible load low-carbon scheduling method of claim 5, wherein the multi-source coordinated optimization force strategy is:
s4.3.1, comparing the wind-solar cost with the time-sharing electricity price, when the electricity price of the power grid is low, preferentially adopting a mode of purchasing electricity to the upper power grid, and calculating the carbon cost;
s4.3.2 considering the charge and discharge efficiency of the energy storage device, if the charge state of the energy storage device is smaller than a set value, charging the energy storage device preferentially, and if the energy storage device reaches the maximum capacity and has surplus electric quantity, selling electricity to a power distribution network; simultaneously detecting the load value of the energy storage device, and stopping discharging when the load value reaches a set minimum value;
s4.3.3, considering the standby load unit, and comprehensively obtaining the optimal unit combined output according to a low-carbon target and an economic strategy.
7. The cloud model-based regional flexible load low-carbon scheduling method of claim 6, wherein the method for analyzing the uncertainty of the demand response by using the cloud model is as follows:
establishing a cloud generator for sample data by using the user demand response quantity after flexible load scheduling:
1) Generating an expected value E based on the sample data n Standard deviation is H e Normal random number E 'of (2)' n ；
2) Generating an expected value E based on the sample data x Standard deviation is abs (E' x ) Normal random number x of (a);
3) Let x be the primary quantization value of qualitative concept, become cloud droplet;
4) Calculating y=exp [ - (x-E) x ) 2 /2(E' n ) 2 ]；
5) Let y be the certainty that x belongs to the qualitative concept;
6) Repeating steps 1) through 5) until P cloud droplets are produced.
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